Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917517569556480 |
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| author | Ferino, Samuel Hoda, Rashina Grundy, John Treude, Christoph |
| author_facet | Ferino, Samuel Hoda, Rashina Grundy, John Treude, Christoph |
| contents | Background: Large Language Models emerged with the potential of provoking a revolution in software development (e.g., automating processes, workforce transformation). Although studies have started to investigate the perceived impact of LLMs for software development, there is a need for empirical studies to comprehend how to balance forward and backward effects of using LLMs.
Objective: We investigated how LLMs impact software development and how to manage the impact from a software developer's perspective.
Method: We conducted 22 interviews with software practitioners across 3 rounds of data collection and analysis, between October (2024) and September (2025). We employed Socio-Technical Grounded Theory for Data Analysis (STGT4DA) to rigorously analyse interview participants' responses.
Results: We identified the benefits (e.g., maintain developer flow, improve developer mental models, and foster entrepreneurship) and challenges (e.g., damage to developers' reputation) of using LLMs at individual, team, organisation, and society levels; as well as actionable guidances into how mitigate these challenges.
Conclusion: Critically, we present the trade-offs that software practitioners, teams, and organisations face in working with LLMs. Our findings are particularly useful for software team leaders and IT managers to assess the viability of LLMs within their specific context. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_06428 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective Ferino, Samuel Hoda, Rashina Grundy, John Treude, Christoph Software Engineering Artificial Intelligence Background: Large Language Models emerged with the potential of provoking a revolution in software development (e.g., automating processes, workforce transformation). Although studies have started to investigate the perceived impact of LLMs for software development, there is a need for empirical studies to comprehend how to balance forward and backward effects of using LLMs. Objective: We investigated how LLMs impact software development and how to manage the impact from a software developer's perspective. Method: We conducted 22 interviews with software practitioners across 3 rounds of data collection and analysis, between October (2024) and September (2025). We employed Socio-Technical Grounded Theory for Data Analysis (STGT4DA) to rigorously analyse interview participants' responses. Results: We identified the benefits (e.g., maintain developer flow, improve developer mental models, and foster entrepreneurship) and challenges (e.g., damage to developers' reputation) of using LLMs at individual, team, organisation, and society levels; as well as actionable guidances into how mitigate these challenges. Conclusion: Critically, we present the trade-offs that software practitioners, teams, and organisations face in working with LLMs. Our findings are particularly useful for software team leaders and IT managers to assess the viability of LLMs within their specific context. |
| title | Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2511.06428 |